ABSTRACT
Before researchers can safely work with statistics in quantitative data analysis, it is important for them to understand some foundational concepts. This chapter addresses: scales of data (nominal, ordinal, interval, ratio); parametric and non-parametric data; descriptive and inferential statistics; kinds of variables (dependent, independent, moderator and mediator, categorical, discrete, continuous); hypotheses (null, alternative); one-tailed and two-tailed tests; confidence intervals; and distributions. The chapter notes that many statistics are based on assumptions about the scales of the data, the nature of the variables and distributions, and it cautions researchers to use the correct statistics with regard to these assumptions and to check that the requirements of the assumptions have been met. Further, it advises researchers to work with as large a sample as possible and indicates reasons for this. Finally, given that many statistics work with Greek letters, the chapter provides an introduction to widely used Greek letters.
